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  • QC'ing NGS data

    Hi all,
    What tools are people using for QC'ing the initial stages of NGS data analysis?

    I'm thinking tools that run on
    - unaligned .fastq
    - aligned .bam
    - single vs. paired end
    - exome seq vs. RNAseq
    - possibly whole-genome, chipseq

    I'm currently using FastQC and picard - but have been thinking of ways to improve on this (which led to the feature list below). I would appreciate tool recommendations and/or suggestions for additional features you like or don't like in your QC tools.

    Thanks,
    -Ben



    Ps. Desired features:

    * FastQC-like
    - user-friendly web interface
    - very easy to download, install, run

    * Picard-like
    - handles both single and paired-end data, aligned and unaligned
    - outputs data tables that are easy to read into R, etc.

    * Extensibility
    - A simple way to add custom graphs and stats would be nice. Although picard and FastQC are both open source and modular, they are written in Java which adds complexity and reduces the potential developer base relative to a language like python (or a toolkit that's not language-specific).
    - An open development model (free/open-source) with more than one active contributor (but strong focus on retaining simplicity and not trying to solve all problems).
    - That being said, ideally the tool would accumulate knowledge about typical problems/failures in common protocols, and where possible, give suggestions on causes rather than just report graphs/stats/warnings and expect users to know what to do. So where possible, aimed at NGS newbies.

    * Holistic
    - at the risk of added complexity, the tool could simultaneously take .fastq and one or more processed bam files from a single sample, and then show both unaliged and aligned stats in one interface, as well as how the stats change as a result of the different processing steps in the NGS pipeline.
    Last edited by bw.; 11-05-2013, 12:33 AM.

  • #2
    Sounds like you want a one-size-fits-most solution. Galaxy comes close:

    Comment


    • #3
      Thanks gringer - I took a look at that Galaxy instance, but didn't see much in the way of QC (other than FastQC).

      Comment


      • #4
        I think FastQC and picard are sort of "enough." But I know there are a few in R. Haven't used them.

        Next generation sequencing (NGS) technologies provide a high-throughput means to generate large amount of sequence data. However, quality control (QC) of sequence data generated from these technologies is extremely important for meaningful downstream analysis. Further, highly efficient and fast processing tools are required to handle the large volume of datasets. Here, we have developed an application, NGS QC Toolkit, for quality check and filtering of high-quality data. This toolkit is a standalone and open source application freely available at http://www.nipgr.res.in/ngsqctoolkit.html. All the tools in the application have been implemented in Perl programming language. The toolkit is comprised of user-friendly tools for QC of sequencing data generated using Roche 454 and Illumina platforms, and additional tools to aid QC (sequence format converter and trimming tools) and analysis (statistics tools). A variety of options have been provided to facilitate the QC at user-defined parameters. The toolkit is expected to be very useful for the QC of NGS data to facilitate better downstream analysis.

        Comment

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